Video Skimming for Quick Browsing Based on Audio and
Image Characterization

Abstract

Digital video is rapidly becoming important for education, entertainment,
and a host of multimedia applications. With the size of the video
collections growing to thousands of hours, technology is needed to
effectively browse segments in a short time without losing the content
of the video. We propose a method to extract the significant audio and
video information and create a "skim" video which represents a very short
synopsis of the original. The goal of this work is to show the utility of
integrating language and image understanding techniques for video skimming
by extraction of significant information, such as specific objects, audio
keywords and relevant video structure. The resulting skim video is much
shorter, where compaction is as high as 20:1, and yet retains the essential
content of the original segment.